Archives of Gerontology and Geriatrics 58 (2014) 364–369
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Frailty, ﬁnancial resources and subjective well-being in later life Ruth E. Hubbard a,*, Victoria A. Goodwin b,c, David J. Llewellyn b,c,d, Krystal Warmoth b,c, Iain A. Lang b,c,d a
Centre for Research in Geriatric Medicine, University of Queensland, Australia University of Exeter Medical School, Exeter, UK c National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care for the South West Peninsula (NIHR PenCLAHRC), UK d M2 Research Group, Exeter, UK b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 13 August 2013 Received in revised form 4 December 2013 Accepted 26 December 2013 Available online 18 January 2014
Though frailty status has recently been linked to poorer quality of life, the impact of income on this relationship has not previously been investigated. Data from a population-based panel study, the English Longitudinal Study of Aging, on 3225 participants aged 65–79 years were analyzed cross-sectionally. A Frailty Index (FI) was determined for each participant as a proportion of accumulated deﬁcits and participants were categorized into four groups on the basis of their FI score: very ﬁt (0.00–0.10), well (0.11–0.14), vulnerable (0.15–0.24), and frail (0.25). Subjective well-being was assessed using the CASP-19 instrument, and levels of ﬁnancial resources quantiﬁed using a range of questions about assets and income from a range of sources. Linear regression models were used to assess the relationship between frailty and well-being. There was a signiﬁcant negative correlation between frailty and wellbeing; the correlation coefﬁcient between FI and CASP-19 scores was 0.58. The relationship was robust to adjustment for sex, age, and relevant health behaviors (smoking and physical activity) and persisted when participants with depressive symptoms were excluded from analysis. Those with greater ﬁnancial resources reported better subjective well-being with evidence of a ‘‘dose–response’’ effect. The poorest participants in each frailty category had similar well-being to the most well-off with worse frailty status. Hence, while the association between frailty and poorer subjective well-being is not signiﬁcantly impacted by higher levels of wealth and income, ﬁnancial resources may provide a partial buffer against the detrimental psychological effects of frailty. ß 2014 Elsevier Ireland Ltd. All rights reserved.
Keywords: Frail elderly FI Well-being Wealth Income Financial resources
1. Introduction Frailty is an important concept in geriatric medicine. It is closely linked to advanced age and disease-related processes yet is a distinct construct (McMillan & Hubbard, 2012). Frailty is increasingly used as a marker of vulnerability, identifying individuals with a diminished capacity to effectively compensate for external stressors. In community-dwelling populations, those who are frail are at increased risk of death, institutionalization, and worsening disability (Fried et al., 2001; Rockwood, Song, & Mitnitski, 2011; Romero-Ortuno & Kenny, 2012). While the deﬁnition and consequences of frailty are well established, there remain very different approaches to its measurement. One approach identiﬁes frailty as a clinical syndrome
* Corresponding author at: Centre for Research in Geriatric Medicine, School of Medicine, The University of Queensland, Princess Alexandra Hospital, Woolloongabba, Brisbane, Queensland 4102, Australia. Tel.: +61 7 3176 5330; fax: +61 7 3176 6945. E-mail address: [email protected]
(R.E. Hubbard). 0167-4943/$ – see front matter ß 2014 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.archger.2013.12.008
or phenotype (a set of signs and symptoms that co-occur to characterize a speciﬁc medical condition). The Fried phenotype, for example, identiﬁes frailty as the presence of 3 of 5 criteria: weight loss, exhaustion, weak grip strength, slow walking speed, low physical activity (Fried et al., 2001). An alternative to phenotypic approaches is to measure frailty based on the clinician’s subjective opinion (Studenski et al., 2004). In a third approach, frailty is conceptualized as a multidimensional risk state measured by the quantity rather than the nature of health problems (Mitnitski, Mogilner, & Rockwood, 2001). In this paradigm, individuals accumulate deﬁcits throughout their lives: the more deﬁcits an individual has, the higher the likelihood they will be frail (Rockwood & Mitnitski, 2007). Understanding frailty has become the focus of extensive research. The associations of frailty with increasing age, female gender, functional dependence and chronic disease are now well described (Walston et al., 2006). Though the relationships among aging, frailty, and psychological well-being have been less comprehensively explored (Fillit & Butler, 2009), a small number of studies have recently linked frailty to poorer quality of life. In 1318 community-dwellers, an index of self-rated health was moderately correlated with frailty (r = 0.49)
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(Lucicesare, Hubbard, Searle, & Rockwood, 2010) and in 1008 older Hispanic adults being frail was signiﬁcantly associated with lower scores on all physical and cognitive health related quality of life scales (Masel, Graham, Reistetter, Markides, & Ottenbacher, 2009). Similarly, in a larger cohort of 5703 participants of the Canadian Study of Health and Aging, frailty was signiﬁcantly associated with poorer psychological well-being scores, independent of age, sex, education, cognition, and mental health (Andrew, Fisk, & Rockwood, 2012). Well-being has also been shown to be crosssectionally associated with cognitive function in older adults (Llewellyn, Lang, Langa, & Huppert, 2008). The impact of ﬁnancial resources on subjective well-being has received more attention, though results are somewhat conﬂicting. While higher socioeconomic status has pervasive positive effects on both health and mortality (Marmot, 2005), the association between objective measures of wealth and psychological well-being is less clear cut. Some report strong positive associations between wealth and quality of life (Rosero-Bixby & Dow, 2009) and, within countries, those with higher incomes tend to be happier (Graham, 2008). Others argue that the overall contribution of economic status to subjective well-being is trivial (Myers, 2000) and that aspirations increase along with earnings such that ‘‘hedonic adaptation’’ and social comparison annul the positive effects of increased income (Easterlin, 2003). Wealth may become more important when individuals face difﬁcult life circumstances. For example, in the US Health and Retirement Study those above the median in total net worth reported a smaller decline in well being after the onset of a disability than their less well-off peers (Smith, Langa, Kabeto, & Ubel, 2005). In this study we had two objectives: ﬁrst, to investigate the association between frailty and subjective well-being in older people; second, to explore the impact of household wealth and income on this relationship. 2. Methods 2.1. Sample
Frailty Indices been extensively investigated by research groups throughout the developed and developing world. In 4721 participants of the Cardiovascular Health Study, for example, the risk of adverse outcomes was deﬁned more precisely by deﬁcit indices than by the frailty phenotype (Kulminski et al., 2008). In Chinese samples comprising 2032 and 13,717 older adults respectively, the FI was used to measure ‘‘biological age’’ (Goggins, Woo, Sham, & Ho, 2005) and evaluated health status in relation to type of death (Dupre, Gu, Warner, & Yi, 2009). More recently, in 29,905 older Europeans, FI was a stronger predictor of mortality than chronological age (Romero-Ortuno & Kenny, 2012). Frailty Indices can be constructed from different numbers and types of variables, allowing comparisons between datasets (Rockwood & Mitnitski, 2007). The included variables need to fulﬁll certain criteria (Searle et al., 2008): they should represent conditions that accumulate with age, should not saturate (i.e. become ubiquitous with age, such as presbycusis), should cross domains, and should be associated with adverse outcomes. In our cohort, 50 deﬁcits comprised the FI (see Appendix), including sensory and functional impairments, self-reported co-morbidities, and a score in the lowest 10% of a composite cognitive function test. Each individual’s deﬁcit points were then summed and divided by the total number of deﬁcits considered, to yield a FI with theoretical range 0–1. Higher values indicated a greater number of problems, and hence greater frailty. Participants were categorized into four groups according to meaningful FI cut-points: very ﬁt (0.00–0.10), well (0.11–0.14), vulnerable (0.15–0.24) and frail (0.25). These FI scores are associated with different descriptors on a Clinical Frailty Scale. In a study of 2305 community-dwellers, increments in this scale were associated with signiﬁcantly increased risks of death and institutionalization (Rockwood et al., 2005). 2.2.2. Subjective well-being Subjective well-being was measured using the CASP-19, a validated measure of psychological well-being in older people (Hyde, Wiggins, Higgs, & Blane, 2003). This self-completed questionnaire uses 19 Likert-scored items to rate quality of life across ontologically grounded areas based on Maslow’s hierarchy of needs (McLeod, 2007): control, autonomy, pleasure, and selfrealization. The scale ranges from 0, which would represent a complete absence of quality of life, to 57, indicative of total satisfaction in all four domains.
We used data from Wave 1 of the English Longitudinal Study of Aging (ELSA, 2002), a nationally representative panel study of 11,392 community-dwelling adults aged 50 and over in England. ELSA participants were recruited from households involved in the Health Survey for England, an annual government-sponsored cross-sectional survey, in 1998, 1999 and 2001. Households were included in ELSA if one or more individuals living there were aged 50 or over. Analyses of socio-demographic characteristics against census results indicated that the ELSA sample was representative of the English population aged 50 and over (Taylor, Conway, & Calderwood, 2003). In this analysis we included adults aged 65–79 who had complete data on frailty, well-being, and household wealth and income (n = 3225).
2.2.3. Household ﬁnancial resources: wealth and income Household wealth was assessed using a range of questions in which participants were asked about household levels of net wealth: ﬁnancial wealth (savings, stocks and bonds, and other investment vehicles) and income from a range of sources (paid employment, returns on investments, pensions, etc.). For the purposes of analysis we divided total net wealth and total net income by quintiles.
2.2.1. Frailty We adopted the multi-dimensional risk state approach to frailty and used a FI to assess the health status of ELSA participants. The FI model employs a well-deﬁned methodology to create an index as a proportion of deﬁcits (Searle, Mitnitski, Gahbauer, Gill, & Rockwood, 2008). One important property of the FI is that the number rather than the nature of deﬁcits can summarize health status. For example, in one study, items that made up the FI were selected randomly without replacement in 1000 iterations. These Frailty Indices yielded comparable estimates of the risk of adverse outcomes (Rockwood, Mitnitski, Song, Steen, & Skoog, 2006).
We used weighted multiple linear regression models to examine whether frailty was associated with subjective wellbeing with or without adjustment for wealth and income. Adjustment was made for a range of potential confounders: sex, age, tobacco smoking (non-smoker versus current smoker), and participation in moderate physical activity at least once per week. Hence in Model 1, the relationship between frailty and well-being was adjusted for age, sex, smoking, and level of physical activity; in Model 2, the relationship between frailty and well-being was adjusted for age, sex, smoking, level of physical activity, net ﬁnancial wealth, and net income. Models were weighted to
R.E. Hubbard et al. / Archives of Gerontology and Geriatrics 58 (2014) 364–369
366 Table 1 Study subject characteristics.
N (%) Sex Age
Engage in moderate physical activity at least once a week Smoking status
Men Women 65–69 70–74 75–79 No Yes Non-smoker Smoker
compensate for survey non-response and to take into account the survey’s complex clustering and stratiﬁcation. In further analyses which excluded those with low mood, mood was measured by the eight-item Center for Epidemiological Studies – Depression (CESD8) scale (Fechner-Bates, Coyne, & Schwenk, 1994) with those scoring four or higher out of eight classiﬁed as having signiﬁcant depressive symptoms. 3. Results Study participants included a slight majority of women (52.3%) and the mean age of participants was 71.0 years. Most participants engaged in moderate physical activity at least once per week and 86.9% were non-smokers (Table 1). FI scores increased with chronological age and were signiﬁcantly higher in those who did not participate in exercise but there was no signiﬁcant difference in frailty levels between smokers and non-smokers. There was a marked association between frailty and poor psychological well-being. The Pearson correlation coefﬁcient between the FI score and CASP-19 score was 0.58. Fig. 1 shows the relationships between clinically meaningful FI categories and wealth divided by quintiles in fully adjusted models. In each frailty category, those with greater wealth and income reported better subjective well-being with evidence of a ‘‘dose–response’’ effect. However, greater ﬁnancial resources could not fully compensate for the impact of poor health status. The poorest participants in each frailty category had similar well-being to the wealthiest with worse frailty status.
1538 1687 1357 1110 758 800 2425 2803 422
Mean FI (95% conﬁdence interval) (47.7) (52.3) (42.1) (34.4) (23.5) (24.8) (75.2) (86.9) (13.1)
0.110 0.138 0.109 0.129 0.145 0.207 0.098 0.123 0.139
(0.104–0.115) (0.133–0.144) (0.103–0.115) (0.123–0.136) (0.137–0.154) (0.197–0.216) (0.094–0.101) (0.118–0.127) (0.127–0.150)
Table 2 shows the outcomes of weighted multiple linear regression models assessing the cross-sectional association between frailty and well-being. Two models are presented. The ﬁrst is adjusted for age, sex, smoking, and level of physical activity; the second is additionally adjusted for level of household wealth and income divided by quintiles. Neither model showed a statistically signiﬁcant relationship between chronological age and well-being. In the ﬁrst model, female sex and being physically active were signiﬁcantly associated with higher well-being scores. In contrast, statistically signiﬁcant negative effects on well-being were seen for smoking and frailty. In the second model, which included net household ﬁnancial wealth and net household income each divided by quintiles, the relationships observed in the ﬁrst model were each attenuated slightly but all remained statistically signiﬁcant. There was no clear relationship between well-being and wealth but we observed a dose–response-type relationship between income and well-being. Adding household wealth and income to the model increased the model R2 from 0.362 to 0.376, suggesting that ﬁnancial resources explained only a small part of the residual variance from the ﬁrst model. When we excluded participants with depressive symptoms from analysis the observed relationships were all broadly similar. 4. Discussion In this large sample of community-dwelling older adults, higher levels of frailty were associated with poorer subjective well-being.
Fig. 1. Mean CASP scores by levels of frailty and wealth in fully adjusted models (with 95% conﬁdence intervals).
R.E. Hubbard et al. / Archives of Gerontology and Geriatrics 58 (2014) 364–369
Table 2 Effects on well-being scores (CASP-19) of frailty (FI) in adjusted models. Model 1 (95% conﬁdence interval) Age Female Moderate activity Current smoker Net ﬁnancial wealth
Least wealthy 2 3 4 Most wealthy Lowest income 2 3 4 Highest income
Frailty Intercept R2
0.04 ( 0.09 to 0.01) 1.40 (0.96–1.85) 1.12 (0.49–1.74) 1.52 ( 2.24 to 0.79) – – – – – – – – – – 35.26 ( 38.00 to 32.52) 49.08 (45.16–53.00) 0.362
Model 2 (95% conﬁdence interval) 0.01 ( 0.06 to 0.05) 1.60 (1.15–2.06) 0.91 (0.29–1.54) 1.11 ( 1.85 to 0.38) 0 0.67 ( 1.52 to 0.19) 0.23 ( 0.63 to 1.09) 0.44 ( 0.42 to 1.31) 0.95 ( 0.03 to 1.87) 0 0.56 ( 0.15 to 1.28) 0.27 ( 0.51 to 1.05) 1.34 (0.51–2.17) 2.04 (1.03–3.04) 34.38 ( 37.13 to 31.63) 45.86 (41.72–50.00) 0.376
Model 1: Relationship between frailty and well-being adjusted for age, sex, smoking, and level of physical activity. Model 2: Relationship between frailty and well-being adjusted for age, sex, smoking, level of physical activity, net ﬁnancial wealth and net income.
The FI correlated well with an established measure of well-being, the CASP-19. Individuals with greater ﬁnancial resources reported better subjective well-being; this association was primarily observed in relation to income rather than wealth though there is likely to be co-linearity between the two. The poorest participants in each frailty category had similar well-being to the wealthiest with worse frailty status, so while the association between frailty and well-being was not signiﬁcantly impacted by greater ﬁnancial resources, ﬁnancial resources may provide a partial buffer against the detrimental psychological effects of frailty. Our ﬁndings should be interpreted with caution. The crosssectional nature of our data precludes the investigation of adverse outcomes and limits the inferences we can draw. Though frailty (Fried et al., 2001; Rockwood et al., 2011; Romero-Ortuno & Kenny, 2012), poor subjective well-being (Lucicesare et al., 2010) and lower socioeconomic status (Rosero-Bixby & Dow, 2009) are all associated with mortality in older people, this study does not enable us to disentangle their prognostic importance. FI scores could not be calculated for some participants due to missing data. Individuals in this group were likely to have lower levels of household wealth and were also older so it is probable they were frailer. The cross-sectional design also denies us the opportunity to investigate the temporal relationship between the associations observed. While the psychological impact of poor health status is not at all surprising and has been well described (Mezuk, Edwards, Lohman, Choi, & Lapane, 2011), reverse causality is also a possibility. Those with more emotional resilience may be less likely to become frail (Fillit & Butler, 2009), with neurobiological mechanisms mediating this pathway. Negative affect in middleaged adults has been directly related to health-relevant biological processes, including increased inﬂammatory activity and greater daily cortisol output (Steptoe, Wardle, & Marmot, 2005). In older people, inﬂammatory processes have been linked to frailty across different deﬁnitions of the term (Hubbard, O’Mahony, Savva, Calver, & Woodhouse, 2009) and elevated cortisol levels can lead to atrophy of the hippocampus, directly impairing cognitive function (McEwen, 2006). Conversely, a positive affect has been associated with a lower risk of frailty development (Ostir, Ottenbacher, & Markides, 2004). Self-perceptions of aging may also mediate a link between poorer well-being and frailty development: in older people, negative perceptions of aging seem to both predict a decline in physical functioning (Sargent-Cox, Anstey, & Luszcz, 2012) and to impact longevity (Levy, Slade, Kunkel, & Kasl, 2002).
Previous investigations of this cohort found individual poverty and neighborhood deprivation to be associated with higher levels of frailty (Lang et al., 2009). Here we found that while the inverse relationship between frailty and well-being persisted in those with higher levels of household wealth, ﬁnancial resources did seem to provide a partial buffer against the detrimental psychological effects of frailty. Potential underlying mechanisms for this ﬁnding are intriguing. Qualitative analyses with reference to Amartya Sen’s work on capability and well-being suggest that the capacity of older people to achieve certain functions (rather than health status itself) is critical to quality of life (Grewal et al., 2006). Financial resources may mitigate impairments and disabilities becoming a handicap, thereby lessening the impact of frail health on well-being. Alternatively, wealth may be an epi-phenomenon, a marker of an additional or alternative psychological safeguard, such as level of education or neighborhood environment. Those with lower individual wealth are likely to live in more deprived areas and may have less established networks of social support. Their social vulnerability (Andrew, Mitnitski, & Rockwood, 2008) may result in less practical help and/or emotional succor to compensate their poor health. Regardless of the mechanisms involved, we found wealth attenuated rather than alleviated the negative psychological impact of frailty. Our ﬁndings also speak against the ‘‘happy peasant’’ hypothesis that, due to low expectations, people living in poverty may report good subjective well-being despite poor health status (Graham, 2008). 5. Conclusions This study contributes to our understanding of the relationship between frailty and subjective well-being in older adults. Here, older people with greater ﬁnancial resources reported better subjective well-being with evidence of a ‘‘dose–response’’ effect. The poorest participants in each frailty category had similar wellbeing to the wealthiest with worse frailty status. Since the pathways to frailty development are complex and currently poorly understood, understanding the impact of frailty is a worthwhile objective. Exploring how the psychological effects of frailty may be mitigated is the focus of further enquiries by our group. Funding This work was supported by the UK National Institute for Health Research (NIHR) Collaboration for Applied Health Research and
R.E. Hubbard et al. / Archives of Gerontology and Geriatrics 58 (2014) 364–369
Care (CLAHRC) for the South West Peninsula. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.
Arthritis (including osteoarthritis, or rheumatism)
Conﬂict of interest statement
Cancer or a malignant tumor (excluding minor skin cancers)
Osteoporosis, sometimes called thin or brittle bones
Parkinson’s disease Any emotional, nervous or psychiatric problems
Appendix. 50 deﬁcits included in English Longitudinal Study of Aging Wave 1 FI Type of deﬁcit
Problems with . . .
Dressing, including putting on shoes or socks Bathing or showering Eating, such as cutting up your food Getting in or out of bed Using the toilet, including getting up or down Using a map to ﬁgure out how to get around in a strange place Preparing a hot meal Shopping for groceries Making telephone calls Taking medications
Alzheimer’s disease Dementia, organic brain syndrome, senility or any other serious memory Eyesight
Fair, poor, or blind (rather than excellent, very good or good)
Fair or poor (rather than excellent, very good or good)
Fallen in the last two years
Ever fractured hip
Ever had any joint replacements
In bottom 10% of sample distribution of cognitive scores on a standardized cognitive battery
Often troubled with severe pain
Problems with balance when walking on a level surface
Problems with dizziness when walking on a level surface
Lost any urine beyond your control in the last 12 months
Bad or very bad
Doing work around the house or garden Managing money Walking across a room Walking 100 yards Walking quarter of a mile Sitting for about two hours Getting up from a chair after sitting for long periods
Climbing a single ﬂight of stairs
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Stooping, kneeling or crouching Reaching or extending your hands above shoulder level Pulling or pushing large objects like a living room chair Lifting or carrying weights over 10 pounds, like a heavy bag of groceries Picking up a 5p coin from a table Has a doctor ever told you that you had:
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Angina A heart attack (including myocardial infarction or coronary thrombosis) Congestive heart failure A heart murmur An abnormal heart rhythm Diabetes or high blood sugar A stroke (cerebral vascular disease) Chronic lung disease such as chronic bronchitis or emphysema
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